class_docstr: A wrapper for torch.optim.Optimizer objects that helps with managing dynamically generated parameters.
  
  :param optim_constructor: a torch.optim.Optimizer
  :param optim_args: a dictionary of learning arguments for the optimizer or a callable that returns
      such dictionaries
  :param clip_args: a dictionary of clip_norm and/or clip_value args or a callable that returns
      such dictionaries
docstr: Get state associated with all the optimizers in the form of a dictionary with
  key-value pairs (parameter name, optim state dicts)
docstr_tok: ["Get","state","associated","with","all","the","optimizers","in","the","form","of","a","dictionary","with","key-value","pairs","(","parameter","name",",","optim","state","dicts",")"]
code: def get_state(self) -> Dict:
      """
      Get state associated with all the optimizers in the form of a dictionary with
      key-value pairs (parameter name, optim state dicts)
      """
      state_dict = {}
      for param in self.optim_objs:
          param_name = pyro.get_param_store().param_name(param)
          state_dict[param_name] = _get_state_dict(
              self.optim_objs[param]
          )
      return state_dict
  
imports: import inspect
  from typing import Any, Callable, Dict, Iterable, List, Optional, Type, Union, ValuesView
  import torch
  from torch import Tensor
  from torch.nn.utils import clip_grad_norm_, clip_grad_value_
  from torch.optim import Optimizer
  import pyro
  from pyro.optim.adagrad_rmsprop import AdagradRMSProp
  from pyro.optim.clipped_adam import ClippedAdam
  from pyro.optim.dct_adam import DCTAdam
  from pyro.params.param_store import module_from_param_with_module_name, normalize_param_name, user_param_name
label_desc: Use image node_id 3 for calling the PyroOptim obj's underlying member method code with example usage: obj.get_state() and returns: state_dict